Work

Radial basis function neural networks: a topical state-of-the-art survey

Year: 2016

Type: article

Abstract: Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors. This paper aims to offer a compendious and sensible survey on RBF networks. The advantages they offer, such as fast training and global approximation capability with local responses, are attracting many researchers to use them in diversified fields. The overall algorithmic development of RBF networks by giving special focus on their learning methods, novel kernels, and fine tuning of kernel parameters have been discussed. In addition, we have considered the recent research work on optimization of multi-criterions in RBF networks and a range of indicative application areas along with some open source RBFN tools.

Source: Open Computer Science

DOI: 10.1515/comp-2016-0005

Authors: Ch. Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri, Sung-Bae Cho

Institutions: Ajou University, Yonsei University

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,187

Funder: National Research Foundation